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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

213
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
213
Three-Winding Transformers01:19

Three-Winding Transformers

314
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
314
Types Of Transformers01:16

Types Of Transformers

1.1K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.1K
The Ideal Transformer01:26

The Ideal Transformer

909
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
909
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

421
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
421
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

807
The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
807

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Related Experiment Video

Updated: Sep 16, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Fault Identification Model Using Convolutional Neural Networks with Transformer Architecture.

Yongxin Fan1, Yiming Dang2, Yangming Guo3

  • 1School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary

This study introduces a hybrid deep learning model for advanced fault detection in automated industrial systems. The framework accurately predicts Remaining Useful Life (RUL) and identifies faults, enhancing machine reliability and safety.

Keywords:
convolutional neural networkfault identificationtransformer

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Area of Science:

  • Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Industrial automation increases efficiency but challenges autonomous fault detection in machines.
  • Undetected machine faults lead to operational disruptions, economic losses, and safety risks.
  • Prognostics and Health Management (PHM) is crucial for reliable industrial systems.

Purpose of the Study:

  • To develop a novel hybrid deep learning framework for autonomous fault detection and Remaining Useful Life (RUL) prediction.
  • To improve the reliability and safety of automated industrial systems through intelligent maintenance.

Main Methods:

  • A hybrid deep learning framework integrating Convolutional Neural Networks (CNN) for feature extraction and Transformer architecture for temporal modeling.
  • Validation using NASA's Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset, featuring multi-sensor data and RUL labels from aeroengines.
  • Learning from time-series sensor data for accurate RUL predictions and early fault detection.

Main Results:

  • The proposed framework achieved over 97% accuracy in RUL prediction and fault detection.
  • Demonstrated robustness and adaptability across single and multiple operating conditions.
  • Successfully learned from time-series sensor data for predictive maintenance.

Conclusions:

  • The hybrid deep learning framework shows significant potential for intelligent maintenance systems.
  • Contributes to the field of Prognostics and Health Management (PHM) for industrial applications.
  • Enables more reliable, efficient, and self-monitoring industrial operations.